مطالعه تطبیقی مسائل و گزینه‌های راهبردی در صنعت هوافضا‌ با استفاده از متن‌کاوی اسناد سیاستی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشکده مدیریت دانشگاه تهران

2 پژوهشکده مطالعات فناوری

3 دانشگاه علامه طباطبایی

چکیده

تحقیق حاضر در پی به‌کارگیری فرایند درس‌آموزی برای تحلیل سیاست‌های توسعه صنعت هوافضا در نه منطقه جهان و یادگیری از آن‌ها است. این تحلیل با به‌کارگیری روش متن‌کاوی روی اسناد سیاستی صنعت هوافضا و دو زیرحوزه سیاستی مرتبط یعنی علم، فناوری و نوآوری و دفاع انجام شده است و قلمرو آن مناطق اتحادیه اروپا، انگلستان، امریکا، برزیل، ترکیه، رژیم صهیونیستی، روسیه، ژاپن-کره جنوبی، و هند- پاکستان بوده است. پس از تحلیل فراوانی واژه‌ها، ترسیم شبکه هم‌رخدادی کلمات و خوشه‌بندی این شبکه برای مجموع اسناد سیاستی گردآوری‌شده، شبکه‌های هم‌رخدادی برای هریک از مناطق نه‌گانه به‌طور مجزا ترسیم و تحلیل شده‌اند و مهم‌ترین نکات سیاستی استخراج شده است. در نهایت مهم‌ترین درس‌آموخته‌ها برای سیاست‌گذاری در صنعت هوافضای جمهوری اسلامی ایران ارائه شده است. جهت‌گیری به سمت تحقیقات کاربردی و کاربردی کردن تحقیقات؛ ارتباط دولت، صنعت و دانشگاه؛ یکپارچه‌سازی سیاست‌ها و برنامه‌ها؛ استفاده از ظرفیت‌های دیپلماسی علم و فناوری؛ استفاده بهینه از منابع؛ برنامه‌ریزی دقیق و زمان‌مند برای نوآوری‌ها و فناوری‌های آینده؛ توسعه برنامه‌های مهارت‌آموزی؛ توجه ویژه و فراگیر به کاربردها و خدمات مشاهده زمین؛ ایجاد خوشه‌های تخصصی هوافضا و استفاده از ظرفیت‌های مناطق؛ تنوع‌بخشی به منابع تأمین مالی؛ تلاش برای بومی‌سازی محصولات و سامانه‌های هوافضا در کشورهای در حال توسعه؛ و نگاه سیستمی به سیاست‌گذاری علم، فناوری و نوآوری درس‌هایی هستند که از اسناد سیاستی تحلیل‌شده در این تحقیق می‌توان آموخت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

A Comparative Study of Development Strategies in A High Tech Industry Through Text Mining of Policy Documents

نویسندگان [English]

  • Mojtaba Talafi 1
  • Mohammad Hosain Shojaei 2
  • Sayyed Amin Taheri 3
1 Management Faculty, University of Tehran
2 Technology Studies Institute
3 Allameh Tabatabaei University
چکیده [English]

The present study seeks to apply Lesson drawing process for analyzing aerospace industry (AI) policies in nine regions/countries and learning from them. This analysis has been carried out by applying the methodology of text mining to policy documents of AI and the two related policy areas, i.e. science, technology and innovation, and defense. The study comprises the regions/countries of the EU,UK,USA,Brazil,Turkey,the Zionist regime,Russia,Japan-Korea,and India-Pakistan. After analyzing the frequency of words, drawing the co-occurrence network of words and clustering this network for all policy documents, the co-occurrence networks for each of the nine countries/regions are separately mapped and analyzed; then the most important policy points are extracted. Finally, the most important lessons learned for AI policy Analysis in the Islamic Republic of Iran are presented. Orientation towards applied research and application of researches; the interaction of government, industry and universities; the integration of policies and programs; the application of science and technology diplomacy capacities; the optimal use of resources; accurate planning for future innovations and technologies; the development of training programs;special attention to space observation services; the creation of aerospace clusters and the use of regional capacities; the diversification of financing sources; the localization of products and systems in developing countries; and a systematic approach to science,Technology and Innovation policy are lessons learned from the analyzed policy documents in this study.

کلیدواژه‌ها [English]

  • Policy analysis
  • Lesson Drawing
  • Text Mining
  • Co-word Analysis
  • Aerospace Industry

Alder, E. and Bernstein, S. 2005. Knowledge in Power: The Epistemic Construction of Global Governance. In M. Barnett and R. Duvall eds. Power in Global Governance. Cambridge, Cambridge University Press.

Altaweel, M. and Bone, C. 2012. Applying content analysis for investigating the reporting of water issues. Computers, Environment and Urban Systems, 36(6), pp. 599-613.

Bailey, C.A. 2007. A guide to qualitative field research: Sage Publications.

Charalabidis, Y. and Loukis, E. 2012. Participative public policy making through multiple social media platforms utilization. International Journal of Electronic Government Research (IJEGR,), 8(3), pp. 78-97.

Cho, J. 2014. Intellectual structure of the institutional repository field: A co-word analysis. Journal of Information Science, 40(3), pp. 386-397.

Choudhary, A.K., Oluikpe, P., Harding, J.A. and Carrillo, P.M. 2009. The needs and benefits of Text Mining applications on Post-Project Reviews. Computers in Industry, 60(9), pp. 728-740.

Common, R. 2001. Public management and policy transfer in Southeast Asia. Aldershot: Ashgate.

Common, R. 2004. Organisational learning in a political environment: Improving policy-making in UK government. Policy studies, 25(1), pp. 35-49.

De Fortuny, E.J., De Smedt, T., Martens, D. and Daelemans, W. 2012. Media coverage in times of political crisis: A text mining approach. Expert Systems with Applications, 39(14), pp. 11616-11622.

Derrick, G., Meijer, I. and Van Wijk, E. 2014. Unwrapping “impact” for evaluation: A co-word analysis of the UK REF2014 policy documents using VOSviewer. Proceedings of the science and technology indicators conference.

Dolowitz, D. and Marsh, D. 1996. Who Learns What from Whom: a Review of the Policy Transfer Literature. Political Studies, 44(2), pp. 343-351.

Dolowitz, D. and Marsh, D. 2000. Learning from Abroad: The Role of Policy Transfer in Contemporary Policy-Making. Governance: An International Journal of Policy and Administration, 3(1),pp. 5- 24.

Dye, T. 2008. Understanding public policy. Pearson: Prentice Hall.

Evans, M. 2004. Policy transfer in global perspective. Aldershot: Ashgate.

Evans, M. 2009. Policy transfer in critical perspective. Policy Studies, 30(3), pp. 243- 268.

Evans, M. and Davies, J. 1999. Understanding policy transfer: A Multi‐level, multi‐disciplinary perspective. Public administration, 77(2), pp. 361-385.

Feng, J., Zhang, Y.Q. and Zhang, H. 2017. Improving the co-word analysis method based on semantic distance. Scientometrics, 111(3), pp. 1521-1531.

Gal-Tzur, A. et al. 2014. The potential of social media in delivering transport policy goals. Transport Policy, Volume (32), pp. 115-123.

Given, L.M. 2008. The Sage encyclopedia of qualitative research methods: Sage Publications.

Haas, P.M. 1989. Do regimes matter? Epistemic communities and Mediterranean pollution control. International organization, 43(3), pp. 377-403.

Hashimi, H., Hafez, A. and Mathkour, H. 2015. Selection criteria for text mining approaches. Computers in Human Behavior, Volume (51), pp. 729-733.

Heclo, H. 2010. Modern social politics in Britain and Sweden: ECPR Press.

Hulme, R. 2005. Policy Transfer and the Internationalisation of Social Policy. Social Policy & Society, 4(4), pp. 417- 425.

Kang, Y., Shin, J. and Park, C. 2016. Assessing climate change risk and adaptation policy improvements through text-mining. Urban Design, Volume (17), pp.  69-84.

Kantardzic, M. 2011. Data mining: concepts, models, methods, and algorithms: John Wiley & Sons.

Kao, A. and Poteet, S.R. 2007. Natural language processing and text mining: Springer Science & Business Media.

Kobayashi, V.B. et al. 2018. Text mining in organizational research. Organizational Research Methods , 21(3), pp. 733-765.

Kraft, M.E. and Furlong, S.R. 2012. Public policy: Politics, analysis, and alternatives: Cq Press.

Kugo, A., Yoshikawa, H., Shimoda, H. and Wakabayashi, Y. 2005. Text mining analysis of public comments regarding high-level radioactive waste disposal. Journal of Nuclear Science and Technology, 42(9), pp.  755-767.

Ladi, S. 2005. Globalisation, policy transfer and policy research institutes. Cheltenham: Edward.

Li, C. 2017. Market Opportunity and Policy Support for Chinese Old Aging Industry: An Application of Text Mining. SHS Web of Conferences, EDP Sciences.

Lim, E.T. 2002. Five trends in presidential rhetoric: An analysis of rhetoric from George Washington to Bill Clinton. Presidential Studies Quarterly, 32(2), pp.  328-348.

Lincoln, Y.S. and Guba, E.G. 1985. Naturalistic inquiry. Beverly Hills, CA: Sage.

Lourenço, R.P. and Costa, J.P. 2007. Incorporating citizens' views in local policy decision making processes. Decision Support Systems, 43(4), pp.  1499-1511.

Merriam, S.B. and Tisdell, E.J. 2009. Qualitative research: A guide to design and implementation: John Wiley & Sons.

Masahiko, O. 2013. Analysis of the Influence on Interregional Migration by the Region's Policy Priority: Measurement of policy priority sensitivities using text mining. Discussion Papers 13072. Research Institute of Economy, Trade and Industry (RIETI).

May, P.J. 1992. Policy learning and failure. Journal of public policy, 12(4), pp.  331-354.

Meseguer, C. and Yebra, C.M. 2009. Learning, policy making, and market reforms: Cambridge University Press.

Monarch, I.A. 2013. Information science and information systems: converging or diverging? Proceedings of the Annual Conference of CAIS/Actes du congrès annuel de l'ACSI.

Ngai, E. and Lee, P. 2016. A Review of the literature on Applications of Text Mining in Policy Making. PACIS.

Oe, H., Yamaoka, Y. and Hideshima, E. 2016. How to Support Decision Making of Local Government in Prioritising Policy Menu Responding to Citizens’ Views: An Exploratory Study of Text Mining Approach to Attain Cognitive Map Based on Citizen Survey Data. International Conference on Decision Support System Technology, Springer.

Park, C. and Yong, T. 2017. Prospect of Korean nuclear policy change through text mining. Energy Procedia, Volume (128), pp. 72-78.

Park, S.J. et al. 2011. Networked politics on Cyworld: The text and sentiment of Korean political profiles. Social Science Computer Review, 29(3), pp. 288-299.

Quinn, K.M. et al. 2010. How to analyze political attention with minimal assumptions and costs. American Journal of Political Science, 54(1), pp.  209-228.

Ravikumar, S., Agrahari, A. and Singh, S. 2015. Mapping the intellectual structure of scientometrics: A co-word analysis of the journal Scientometrics (2005–2010). Scientometrics, 102(1), pp.  929-955.

Rose, R. 1991. What is Lesson-Drawing? Journal of Public Policy, 11(1), pp. 3- 30.

Rose, R. 1993. Lesson-Drawing in Public Policy: A Guide to Learning across Time and Space. Chatham, NJ: Chatham House Publishers.

Rose, R. 2005. Learning from Comparative Public Policy: A Practical Guide. London: Routledge.

Sabatier, P.A. 1988. An advocacy coalition framework of policy change and the role of policy-oriented learning therein. Policy sciences, 21(2-3), pp. 129-168.

Schneider, A. and Ingram, H. 1988. Systematically pinching ideas: A comparative approach to policy design. Journal of public policy, 8(1), pp.  61-80.

Smith, K.B. and Larimer, C.W. 2009. The public policy theory primer: Westview Press.

Sobkowicz, P., Kaschesky, M. and Bouchard, G. 2012. Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web. Government Information Quarterly, 29(4), pp.  470-479.

Stiglitz, J. 2000. Scan Globally, Reinvent Locally: Knowledge Infrastructure and the Localization of Knowledge, in: D. Stone (ed.) Banking on Knowledge: The Genesis of the Global Development Network. London: Routledge.

Stone, D. 2000. Non-Governmental Policy Transfer: The Strategies of Independent Policy Institutes. Governance: An International Journal of Policy and Administration, 13(1), pp.  45- 62.

Stone, D. 2001. Learning Lessons, Policy Transfer and the International Diffusion of Policy Ideas. University of Warwick.

Stone, D. 2003. Banking on knowledge: the genesis of the Global Development Network: Routledge.

Tadashi, M. 2016. Policy Agenda and Local Assembly Reform in the North Kanto Region: Analysis of the Minutes of the Seven Major City Assemblies using Text-mining Approach. Studies of regional policy, 18(2/3), pp. 33-49.

Talamini, E. and Dewes, H. 2012. The macro-environment for liquid biofuels in Brazilian science and public policies. Science and Public Policy, 39(1), pp. 13-29.

Talamini, E., Wubben, E.F. and Dewes, H. 2013. The Macro-Environment for Liquid Biofuels in German Science, Mass Media and Government. Review of European Studies, 5(2), p. 33.

Thelwall, M., Vann, K. and Fairclough, R. 2006. Web issue analysis: An integrated water resource management case study. Journal of the American Society for information Science and Technology, 57(10), pp.1303-1314.

Velasquez, J.D. and Gonzalez, P. 2010. Expanding the possibilities of deliberation: The use of data mining for strengthening democracy with an application to education reform. The Information Society ,26(1), pp. 1-16.

Wen, M. 2018. A Co-Word Analysis on Policy of Business Incubator in Guangdong Province. Open Journal of Business and Management, Volume (6), pp.  214-224.

Whittington, R. 2001. What is strategy-and does it matter? 2 ed: Cengage Learning.

Wolman, H. 1992. Understanding cross national policy transfers: the case of Britain and the US. Governance, 5(1), pp.  27-45.

Wormell, I. 2000. Critical aspects of the Danish welfare state as revealed by issue tracking. Scientometrics, 48(2), pp.  237-250.

Wu, S. and Chu, S. 2013. The text mining and classification analyses on the relationship of Macau special administrative region's policy addresses from 2012 to 2013. International Conference on Engineering, Management Science and Innovation (ICEMSI), IEEE.

Yan, B.-N., Lee, T.-S. and Lee, T.-P. 2015. Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis. Scientometrics, 105(2), pp. 1285-1300.

بنیاد توسعه علوم و فناوری‌های هوافضا ۱۳۹۳. روش‌شناسی تدوین اسناد سیاست‌گذاری و ره‌نگاشت با نگاه به بخش هوافضا. تهران: بنیاد توسعه علوم و فناوری‌های هوافضا.

پورعزت، ع.ا. و سوداگر، ه. 1391. تبیین فراگرد شکل‌گیری استراتژی در سازمان‌های دولتی فعال در عرصه سیاسی ج.ا. ایران. پژوهش‌های مدیریت در ایران 16(2)، صص. 35-56.

حاجی‌پور، ب. و ناجی، م. 1390. گونه‌شناسی شکل‌گیری استراتژی در سازمان‌های دولتی فعال در عرصه اقتصادی جمهوری اسلامی ایران. اندیشه مدیریت راهبردی 5(1)، صص. 99-124.